{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "general-measurement",
   "metadata": {},
   "source": [
    "## 线性回归"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "proud-globe",
   "metadata": {},
   "source": [
    "### 一元线性回归"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "featured-roulette",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np \n",
    "import pandas as pd \n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "representative-signature",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([3.15645915, 4.53030677, 2.66982264, 1.08928182, 8.68166479,\n",
       "       6.29728518, 3.52518708, 0.67537599, 6.26350592, 5.9866086 ,\n",
       "       0.92217251, 0.56851551, 2.371322  , 2.3540367 , 7.74371382,\n",
       "       0.42601131, 3.54695279, 4.69166604, 7.6757992 , 8.63516638,\n",
       "       7.77620476, 2.25900156, 7.60389215, 4.41385371, 2.17436735,\n",
       "       6.20251314, 1.4122351 , 0.88755181, 4.37561248, 2.89188192,\n",
       "       2.73069231, 5.49044754, 9.65158649, 6.92406344, 6.44791747,\n",
       "       7.25829272, 9.383001  , 0.47294471, 0.02190553, 3.46374663,\n",
       "       6.7797387 , 3.14115205, 7.83066568, 6.05550374, 1.12060886,\n",
       "       8.86877968, 3.42211267, 5.51175913, 8.15684753, 8.54537963])"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#生成横坐标 范围0-10\n",
    "np.random.seed(420)\n",
    "x = np.random.rand(50)*10\n",
    "x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "beneficial-cookbook",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 0.82752744,  2.15545969, -0.18657652, -3.13498238, 12.69403608,\n",
       "        8.5331076 ,  1.482163  , -3.84302328,  8.83981668,  8.65446819,\n",
       "       -3.92379092, -4.62608691, -0.71473145, -2.44734311, 11.00259711,\n",
       "       -3.76876776,  1.39397186,  5.09225598, 10.00361301, 13.81622057,\n",
       "       11.09921857, -1.76687388,  8.46958143,  3.31288229, -0.28122168,\n",
       "        5.69282924, -0.84698453, -2.37309187,  2.57389993,  1.91098401,\n",
       "        0.95737669,  7.31105401, 14.9512938 ,  6.8498008 ,  8.23610148,\n",
       "        9.81956274, 12.74792212, -3.68438581, -5.94744827,  3.09889911,\n",
       "       10.84641511,  2.38776228, 10.09586882,  7.81862076, -2.59372481,\n",
       "       12.00685709,  1.31456913,  5.42748426, 10.66297504, 12.10833264])"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#计算y，并添加扰动项\n",
    "np.random.seed(420)  #----加这个随机种子啥意思？\n",
    "y = 2*x - 5 + np.random.randn(50) #----rand:正态分布下的随机数\n",
    "y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "advance-worry",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x1be1f6bc0f0>]"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(x,y,'o')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fresh-particle",
   "metadata": {},
   "source": [
    "### 导入线性回归模型"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "aggressive-indiana",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.linear_model import LinearRegression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "working-yorkshire",
   "metadata": {},
   "outputs": [],
   "source": [
    "#实例化\n",
    "reg = LinearRegression(fit_intercept=True) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "fossil-score",
   "metadata": {},
   "outputs": [],
   "source": [
    "#先转化为一维数组才能训练\n",
    "x2 = x.reshape(-1,1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "crucial-product",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([2.02606953]), -5.163803042721518)"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#训练模型\n",
    "reg.fit(x2,y)\n",
    "reg.coef_ ,reg.intercept_ # 查看方程系数---斜率   # 查看系数--截距"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "suffering-cabin",
   "metadata": {},
   "source": [
    "#将拟合好的直线画出来"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "quality-parker",
   "metadata": {},
   "outputs": [],
   "source": [
    "#生成绘制直线的横坐标\n",
    "xfit = np.linspace(0,10,100)#从0到10，生成100个\n",
    "xfit2 = xfit.reshape(-1,1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "stable-monte",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([-5.16380304, -4.95914955, -4.75449607, -4.54984258, -4.34518909,\n",
       "       -4.1405356 , -3.93588211, -3.73122863, -3.52657514, -3.32192165,\n",
       "       -3.11726816, -2.91261467, -2.70796118, -2.5033077 , -2.29865421,\n",
       "       -2.09400072, -1.88934723, -1.68469374, -1.48004025, -1.27538677,\n",
       "       -1.07073328, -0.86607979, -0.6614263 , -0.45677281, -0.25211933,\n",
       "       -0.04746584,  0.15718765,  0.36184114,  0.56649463,  0.77114812,\n",
       "        0.9758016 ,  1.18045509,  1.38510858,  1.58976207,  1.79441556,\n",
       "        1.99906904,  2.20372253,  2.40837602,  2.61302951,  2.817683  ,\n",
       "        3.02233649,  3.22698997,  3.43164346,  3.63629695,  3.84095044,\n",
       "        4.04560393,  4.25025742,  4.4549109 ,  4.65956439,  4.86421788,\n",
       "        5.06887137,  5.27352486,  5.47817834,  5.68283183,  5.88748532,\n",
       "        6.09213881,  6.2967923 ,  6.50144579,  6.70609927,  6.91075276,\n",
       "        7.11540625,  7.32005974,  7.52471323,  7.72936671,  7.9340202 ,\n",
       "        8.13867369,  8.34332718,  8.54798067,  8.75263416,  8.95728764,\n",
       "        9.16194113,  9.36659462,  9.57124811,  9.7759016 ,  9.98055508,\n",
       "       10.18520857, 10.38986206, 10.59451555, 10.79916904, 11.00382253,\n",
       "       11.20847601, 11.4131295 , 11.61778299, 11.82243648, 12.02708997,\n",
       "       12.23174346, 12.43639694, 12.64105043, 12.84570392, 13.05035741,\n",
       "       13.2550109 , 13.45966438, 13.66431787, 13.86897136, 14.07362485,\n",
       "       14.27827834, 14.48293183, 14.68758531, 14.8922388 , 15.09689229])"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "yfit = reg.predict(xfit2)\n",
    "yfit"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "earned-grace",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x1be2a6d2588>]"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(xfit,yfit)#画出点的直线\n",
    "plt.plot(x,y,'o')#画出原来训练集的点"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "settled-bargain",
   "metadata": {},
   "source": [
    "### 通常使用MSE和R2评估拟合效果"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "hungry-walter",
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'data' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-31-e3cc393fd43c>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m      1\u001b[0m \u001b[1;32mfrom\u001b[0m \u001b[0msklearn\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mmetrics\u001b[0m \u001b[1;32mimport\u001b[0m \u001b[0mmean_squared_error\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mr2_score\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 2\u001b[1;33m \u001b[0myhat\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mreg\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mpredict\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0miloc\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m-\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      3\u001b[0m \u001b[0mmean_squared_error\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0my\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0myhat\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      4\u001b[0m \u001b[0mr2_score\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0my\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0myhat\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mNameError\u001b[0m: name 'data' is not defined"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import mean_squared_error,r2_score \n",
    "yhat = reg.predict(data.iloc[:,:-1])\n",
    "mean_squared_error(y,yhat) \n",
    "r2_score(y,yhat)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "collected-institution",
   "metadata": {},
   "source": [
    "## 房价预测"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "christian-twins",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.linear_model import LinearRegression\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.model_selection import cross_val_score\n",
    "from sklearn.datasets import fetch_california_housing\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "informal-oracle",
   "metadata": {},
   "source": [
    "### 下载数据"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "affected-defensive",
   "metadata": {},
   "outputs": [],
   "source": [
    "housevalue = fetch_california_housing()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "under-basis",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>MedInc</th>\n",
       "      <th>HouseAge</th>\n",
       "      <th>AveRooms</th>\n",
       "      <th>AveBedrms</th>\n",
       "      <th>Population</th>\n",
       "      <th>AveOccup</th>\n",
       "      <th>Latitude</th>\n",
       "      <th>Longitude</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>8.3252</td>\n",
       "      <td>41.0</td>\n",
       "      <td>6.984127</td>\n",
       "      <td>1.023810</td>\n",
       "      <td>322.0</td>\n",
       "      <td>2.555556</td>\n",
       "      <td>37.88</td>\n",
       "      <td>-122.23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>8.3014</td>\n",
       "      <td>21.0</td>\n",
       "      <td>6.238137</td>\n",
       "      <td>0.971880</td>\n",
       "      <td>2401.0</td>\n",
       "      <td>2.109842</td>\n",
       "      <td>37.86</td>\n",
       "      <td>-122.22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>7.2574</td>\n",
       "      <td>52.0</td>\n",
       "      <td>8.288136</td>\n",
       "      <td>1.073446</td>\n",
       "      <td>496.0</td>\n",
       "      <td>2.802260</td>\n",
       "      <td>37.85</td>\n",
       "      <td>-122.24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>5.6431</td>\n",
       "      <td>52.0</td>\n",
       "      <td>5.817352</td>\n",
       "      <td>1.073059</td>\n",
       "      <td>558.0</td>\n",
       "      <td>2.547945</td>\n",
       "      <td>37.85</td>\n",
       "      <td>-122.25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>3.8462</td>\n",
       "      <td>52.0</td>\n",
       "      <td>6.281853</td>\n",
       "      <td>1.081081</td>\n",
       "      <td>565.0</td>\n",
       "      <td>2.181467</td>\n",
       "      <td>37.85</td>\n",
       "      <td>-122.25</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   MedInc  HouseAge  AveRooms  AveBedrms  Population  AveOccup  Latitude  \\\n",
       "0  8.3252      41.0  6.984127   1.023810       322.0  2.555556     37.88   \n",
       "1  8.3014      21.0  6.238137   0.971880      2401.0  2.109842     37.86   \n",
       "2  7.2574      52.0  8.288136   1.073446       496.0  2.802260     37.85   \n",
       "3  5.6431      52.0  5.817352   1.073059       558.0  2.547945     37.85   \n",
       "4  3.8462      52.0  6.281853   1.081081       565.0  2.181467     37.85   \n",
       "\n",
       "   Longitude  \n",
       "0    -122.23  \n",
       "1    -122.22  \n",
       "2    -122.24  \n",
       "3    -122.25  \n",
       "4    -122.25  "
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X = pd.DataFrame(housevalue.data,columns=housevalue.feature_names)\n",
    "X.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "about-chinese",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([4.526, 3.585, 3.521, ..., 0.923, 0.847, 0.894])"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y = housevalue.target\n",
    "y"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "extraordinary-space",
   "metadata": {},
   "source": [
    "### 拆分数据集（训练集和测试集）"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "national-diary",
   "metadata": {},
   "outputs": [],
   "source": [
    "Xtrain,Xtest,Ytrain,Ytest = train_test_split(X,y,test_size = 0.3,random_state = 420)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "intellectual-healing",
   "metadata": {},
   "source": [
    "### 线性回归建模"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "joint-kennedy",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LinearRegression(copy_X=True, fit_intercept=True, n_jobs=None, normalize=False)"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lr = LinearRegression()\n",
    "lr.fit(Xtrain,Ytrain)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "early-export",
   "metadata": {},
   "source": [
    "### 模型评估"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "specified-comparison",
   "metadata": {},
   "source": [
    "#### MSE均方误差 ----越趋于0效果越好"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "virgin-concentrate",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import mean_squared_error"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "covered-surveillance",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.5218522662533102"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_pred = lr.predict(Xtrain)#得到预测结果\n",
    " #-------评估训练集拟合情况，参数1：真实；参数2：预测\n",
    "mean_squared_error(Ytrain,y_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "straight-disposition",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.5309012639324573"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#评估测试集拟合情况\n",
    "y_test_pred = lr.predict(Xtest)#得到预测结果\n",
    "mean_squared_error(Ytest,y_test_pred)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "athletic-pendant",
   "metadata": {},
   "source": [
    "#### MSE交叉验证"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "found-sheep",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['accuracy',\n",
       " 'adjusted_mutual_info_score',\n",
       " 'adjusted_rand_score',\n",
       " 'average_precision',\n",
       " 'balanced_accuracy',\n",
       " 'completeness_score',\n",
       " 'explained_variance',\n",
       " 'f1',\n",
       " 'f1_macro',\n",
       " 'f1_micro',\n",
       " 'f1_samples',\n",
       " 'f1_weighted',\n",
       " 'fowlkes_mallows_score',\n",
       " 'homogeneity_score',\n",
       " 'jaccard',\n",
       " 'jaccard_macro',\n",
       " 'jaccard_micro',\n",
       " 'jaccard_samples',\n",
       " 'jaccard_weighted',\n",
       " 'max_error',\n",
       " 'mutual_info_score',\n",
       " 'neg_brier_score',\n",
       " 'neg_log_loss',\n",
       " 'neg_mean_absolute_error',\n",
       " 'neg_mean_gamma_deviance',\n",
       " 'neg_mean_poisson_deviance',\n",
       " 'neg_mean_squared_error',\n",
       " 'neg_mean_squared_log_error',\n",
       " 'neg_median_absolute_error',\n",
       " 'neg_root_mean_squared_error',\n",
       " 'normalized_mutual_info_score',\n",
       " 'precision',\n",
       " 'precision_macro',\n",
       " 'precision_micro',\n",
       " 'precision_samples',\n",
       " 'precision_weighted',\n",
       " 'r2',\n",
       " 'recall',\n",
       " 'recall_macro',\n",
       " 'recall_micro',\n",
       " 'recall_samples',\n",
       " 'recall_weighted',\n",
       " 'roc_auc',\n",
       " 'roc_auc_ovo',\n",
       " 'roc_auc_ovo_weighted',\n",
       " 'roc_auc_ovr',\n",
       " 'roc_auc_ovr_weighted',\n",
       " 'v_measure_score']"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import sklearn\n",
    "sorted(sklearn.metrics.SCORERS.keys())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "stylish-winner",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([-0.52730876, -0.50816696, -0.48736401, -0.49269076, -0.56611205,\n",
       "       -0.53795641, -0.48253409, -0.5130032 , -0.53188562, -0.60443733])"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lr2 = LinearRegression()\n",
    "#交叉验证使用MSE指标\n",
    "cross_val_score(lr2,Xtrain,Ytrain,cv= 10,scoring='neg_mean_squared_error')#score处选择用什么指标进行交叉验证"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "desirable-clerk",
   "metadata": {},
   "source": [
    "#### MAE绝对均值误差---与MSE差不多，两个选一个用即可"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "lesbian-desperate",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.5309427617356031"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.metrics import mean_absolute_error\n",
    "mean_absolute_error(Ytrain,y_pred)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "assisted-manual",
   "metadata": {},
   "source": [
    "#### MAE交叉验证"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "suited-pharmacy",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([-0.52722504, -0.52321403, -0.52579163, -0.52038755, -0.5431045 ,\n",
       "       -0.53622198, -0.5218329 , -0.53052166, -0.53266059, -0.55297171])"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cross_val_score(lr2,Xtrain,Ytrain,cv= 10,scoring='neg_mean_absolute_error')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "thermal-first",
   "metadata": {},
   "source": [
    "#### R方----即方差，回归中最常用的指标。衡量数据集包含了多少信息量"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "illegal-being",
   "metadata": {},
   "source": [
    "越趋于1拟合效果越好。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "delayed-buddy",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.6067440341875014"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.metrics import r2_score\n",
    "r2_score(Ytrain,y_pred)#训练集的R2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "right-wisconsin",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.6043668160178816"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "r2_score(Ytest,y_test_pred)#测试集的R2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "id": "chubby-group",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.6067440341875014"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lr.score(Xtrain,Ytrain)###---SCORE结果直接就是R2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "id": "powered-aberdeen",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.6043668160178816"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lr.score(Xtest,Ytest)###---SCORE结果直接就是R2"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "hindu-thread",
   "metadata": {},
   "source": [
    "#### R2交叉验证"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "id": "ultimate-logic",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.6039238235546337"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cross_val_score(lr,Xtrain,Ytrain,cv= 10,scoring='r2').mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "supreme-train",
   "metadata": {},
   "source": [
    "#### 查看模型系数"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "id": "civilian-picking",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 4.37358931e-01,  1.02112683e-02, -1.07807216e-01,  6.26433828e-01,\n",
       "        5.21612535e-07, -3.34850965e-03, -4.13095938e-01, -4.26210954e-01])"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lr.coef_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "id": "native-bracelet",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-36.25689322920381"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lr.intercept_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "id": "palestinian-latino",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[('MedInc', 0.43735893059684006),\n",
       " ('HouseAge', 0.010211268294493883),\n",
       " ('AveRooms', -0.10780721617317668),\n",
       " ('AveBedrms', 0.6264338275363759),\n",
       " ('Population', 5.216125353348089e-07),\n",
       " ('AveOccup', -0.003348509646333704),\n",
       " ('Latitude', -0.4130959378947717),\n",
       " ('Longitude', -0.4262109536208464)]"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "list(zip(X.columns,lr.coef_))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "christian-silicon",
   "metadata": {},
   "source": [
    "### 可以将数据集标准化之后再训练"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "id": "nuclear-cooling",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import StandardScaler"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "id": "intimate-warrior",
   "metadata": {},
   "outputs": [],
   "source": [
    "std = StandardScaler()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "id": "fourth-ceiling",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 0.15962565,  0.51019125, -0.40038616, ...,  0.02097226,\n",
       "         0.86530441, -1.30925728],\n",
       "       [ 0.7616735 ,  0.74878657,  0.33049412, ..., -0.03103851,\n",
       "         0.88874515, -1.36422214],\n",
       "       [-1.01128797, -0.20559469,  0.1327176 , ..., -0.0140286 ,\n",
       "         0.180835  ,  0.24474943],\n",
       "       ...,\n",
       "       [-1.11293105, -0.28512647, -0.60376949, ...,  0.08720641,\n",
       "        -1.37094154,  1.23411704],\n",
       "       [-0.16165875,  0.35112771, -0.00439378, ..., -0.02140349,\n",
       "         1.21222727, -1.45916146],\n",
       "       [ 0.6221305 ,  0.51019125, -0.06466591, ..., -0.00489167,\n",
       "        -0.84586911,  0.80938852]])"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#对训练集进行标准化\n",
    "X_train_std = std.fit_transform(Xtrain)\n",
    "X_train_std"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "id": "welcome-weight",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>MedInc</th>\n",
       "      <th>HouseAge</th>\n",
       "      <th>AveRooms</th>\n",
       "      <th>AveBedrms</th>\n",
       "      <th>Population</th>\n",
       "      <th>AveOccup</th>\n",
       "      <th>Latitude</th>\n",
       "      <th>Longitude</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>17073</th>\n",
       "      <td>4.1776</td>\n",
       "      <td>35.0</td>\n",
       "      <td>4.425172</td>\n",
       "      <td>1.030683</td>\n",
       "      <td>5380.0</td>\n",
       "      <td>3.368817</td>\n",
       "      <td>37.48</td>\n",
       "      <td>-122.19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16956</th>\n",
       "      <td>5.3261</td>\n",
       "      <td>38.0</td>\n",
       "      <td>6.267516</td>\n",
       "      <td>1.089172</td>\n",
       "      <td>429.0</td>\n",
       "      <td>2.732484</td>\n",
       "      <td>37.53</td>\n",
       "      <td>-122.30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20012</th>\n",
       "      <td>1.9439</td>\n",
       "      <td>26.0</td>\n",
       "      <td>5.768977</td>\n",
       "      <td>1.141914</td>\n",
       "      <td>891.0</td>\n",
       "      <td>2.940594</td>\n",
       "      <td>36.02</td>\n",
       "      <td>-119.08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13072</th>\n",
       "      <td>2.5000</td>\n",
       "      <td>22.0</td>\n",
       "      <td>4.916000</td>\n",
       "      <td>1.012000</td>\n",
       "      <td>733.0</td>\n",
       "      <td>2.932000</td>\n",
       "      <td>38.57</td>\n",
       "      <td>-121.31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8457</th>\n",
       "      <td>3.8250</td>\n",
       "      <td>34.0</td>\n",
       "      <td>5.036765</td>\n",
       "      <td>1.098039</td>\n",
       "      <td>1134.0</td>\n",
       "      <td>2.779412</td>\n",
       "      <td>33.91</td>\n",
       "      <td>-118.35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12684</th>\n",
       "      <td>2.9250</td>\n",
       "      <td>19.0</td>\n",
       "      <td>5.649321</td>\n",
       "      <td>1.117647</td>\n",
       "      <td>748.0</td>\n",
       "      <td>1.692308</td>\n",
       "      <td>38.55</td>\n",
       "      <td>-121.40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17007</th>\n",
       "      <td>5.0081</td>\n",
       "      <td>39.0</td>\n",
       "      <td>5.013746</td>\n",
       "      <td>0.979381</td>\n",
       "      <td>761.0</td>\n",
       "      <td>2.615120</td>\n",
       "      <td>37.54</td>\n",
       "      <td>-122.29</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2241</th>\n",
       "      <td>5.6445</td>\n",
       "      <td>10.0</td>\n",
       "      <td>6.545866</td>\n",
       "      <td>1.044168</td>\n",
       "      <td>2712.0</td>\n",
       "      <td>3.071348</td>\n",
       "      <td>36.83</td>\n",
       "      <td>-119.81</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7360</th>\n",
       "      <td>2.0162</td>\n",
       "      <td>28.0</td>\n",
       "      <td>3.615464</td>\n",
       "      <td>0.998969</td>\n",
       "      <td>3740.0</td>\n",
       "      <td>3.855670</td>\n",
       "      <td>33.96</td>\n",
       "      <td>-118.19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19676</th>\n",
       "      <td>2.4183</td>\n",
       "      <td>7.0</td>\n",
       "      <td>4.582927</td>\n",
       "      <td>1.082927</td>\n",
       "      <td>1065.0</td>\n",
       "      <td>2.597561</td>\n",
       "      <td>39.16</td>\n",
       "      <td>-121.63</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13679</th>\n",
       "      <td>5.3366</td>\n",
       "      <td>4.0</td>\n",
       "      <td>6.083688</td>\n",
       "      <td>0.967376</td>\n",
       "      <td>1981.0</td>\n",
       "      <td>2.809929</td>\n",
       "      <td>34.04</td>\n",
       "      <td>-117.24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19253</th>\n",
       "      <td>4.7450</td>\n",
       "      <td>28.0</td>\n",
       "      <td>6.313933</td>\n",
       "      <td>1.077601</td>\n",
       "      <td>1634.0</td>\n",
       "      <td>2.881834</td>\n",
       "      <td>38.46</td>\n",
       "      <td>-122.81</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11126</th>\n",
       "      <td>3.0250</td>\n",
       "      <td>22.0</td>\n",
       "      <td>4.006803</td>\n",
       "      <td>0.982993</td>\n",
       "      <td>865.0</td>\n",
       "      <td>2.942177</td>\n",
       "      <td>33.85</td>\n",
       "      <td>-117.91</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2060</th>\n",
       "      <td>2.9013</td>\n",
       "      <td>19.0</td>\n",
       "      <td>6.090909</td>\n",
       "      <td>1.107656</td>\n",
       "      <td>1257.0</td>\n",
       "      <td>3.007177</td>\n",
       "      <td>36.65</td>\n",
       "      <td>-119.74</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12094</th>\n",
       "      <td>5.4625</td>\n",
       "      <td>9.0</td>\n",
       "      <td>6.999071</td>\n",
       "      <td>1.044568</td>\n",
       "      <td>3450.0</td>\n",
       "      <td>3.203343</td>\n",
       "      <td>33.85</td>\n",
       "      <td>-117.40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19832</th>\n",
       "      <td>1.5223</td>\n",
       "      <td>33.0</td>\n",
       "      <td>4.722222</td>\n",
       "      <td>1.026820</td>\n",
       "      <td>2030.0</td>\n",
       "      <td>3.888889</td>\n",
       "      <td>36.54</td>\n",
       "      <td>-119.38</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15418</th>\n",
       "      <td>3.9815</td>\n",
       "      <td>26.0</td>\n",
       "      <td>6.072508</td>\n",
       "      <td>1.048338</td>\n",
       "      <td>1160.0</td>\n",
       "      <td>3.504532</td>\n",
       "      <td>33.22</td>\n",
       "      <td>-117.25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4891</th>\n",
       "      <td>2.1957</td>\n",
       "      <td>33.0</td>\n",
       "      <td>3.254369</td>\n",
       "      <td>1.019417</td>\n",
       "      <td>2564.0</td>\n",
       "      <td>4.978641</td>\n",
       "      <td>34.02</td>\n",
       "      <td>-118.25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14605</th>\n",
       "      <td>4.2200</td>\n",
       "      <td>33.0</td>\n",
       "      <td>6.278689</td>\n",
       "      <td>1.036885</td>\n",
       "      <td>1355.0</td>\n",
       "      <td>2.776639</td>\n",
       "      <td>32.81</td>\n",
       "      <td>-117.17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20315</th>\n",
       "      <td>3.6167</td>\n",
       "      <td>17.0</td>\n",
       "      <td>3.154930</td>\n",
       "      <td>0.985915</td>\n",
       "      <td>147.0</td>\n",
       "      <td>2.070423</td>\n",
       "      <td>34.12</td>\n",
       "      <td>-119.16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10296</th>\n",
       "      <td>6.3303</td>\n",
       "      <td>14.0</td>\n",
       "      <td>6.028358</td>\n",
       "      <td>0.998507</td>\n",
       "      <td>1905.0</td>\n",
       "      <td>2.843284</td>\n",
       "      <td>33.92</td>\n",
       "      <td>-117.87</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3385</th>\n",
       "      <td>5.6184</td>\n",
       "      <td>29.0</td>\n",
       "      <td>5.885417</td>\n",
       "      <td>1.006944</td>\n",
       "      <td>1543.0</td>\n",
       "      <td>2.678819</td>\n",
       "      <td>34.24</td>\n",
       "      <td>-118.28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12241</th>\n",
       "      <td>1.9420</td>\n",
       "      <td>31.0</td>\n",
       "      <td>4.311606</td>\n",
       "      <td>0.998410</td>\n",
       "      <td>1519.0</td>\n",
       "      <td>2.414944</td>\n",
       "      <td>33.74</td>\n",
       "      <td>-116.97</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7174</th>\n",
       "      <td>2.7432</td>\n",
       "      <td>35.0</td>\n",
       "      <td>4.695652</td>\n",
       "      <td>1.112319</td>\n",
       "      <td>1423.0</td>\n",
       "      <td>5.155797</td>\n",
       "      <td>34.05</td>\n",
       "      <td>-118.19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17431</th>\n",
       "      <td>1.9257</td>\n",
       "      <td>27.0</td>\n",
       "      <td>4.203036</td>\n",
       "      <td>1.096774</td>\n",
       "      <td>1544.0</td>\n",
       "      <td>2.929791</td>\n",
       "      <td>34.65</td>\n",
       "      <td>-120.45</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8984</th>\n",
       "      <td>5.9641</td>\n",
       "      <td>44.0</td>\n",
       "      <td>4.357143</td>\n",
       "      <td>1.028571</td>\n",
       "      <td>156.0</td>\n",
       "      <td>2.228571</td>\n",
       "      <td>33.99</td>\n",
       "      <td>-118.44</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1990</th>\n",
       "      <td>1.7011</td>\n",
       "      <td>45.0</td>\n",
       "      <td>4.057018</td>\n",
       "      <td>1.013158</td>\n",
       "      <td>797.0</td>\n",
       "      <td>3.495614</td>\n",
       "      <td>36.73</td>\n",
       "      <td>-119.80</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>306</th>\n",
       "      <td>1.7056</td>\n",
       "      <td>49.0</td>\n",
       "      <td>5.085502</td>\n",
       "      <td>1.048327</td>\n",
       "      <td>790.0</td>\n",
       "      <td>2.936803</td>\n",
       "      <td>37.76</td>\n",
       "      <td>-122.19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20342</th>\n",
       "      <td>5.0145</td>\n",
       "      <td>21.0</td>\n",
       "      <td>6.310811</td>\n",
       "      <td>1.038610</td>\n",
       "      <td>4199.0</td>\n",
       "      <td>2.702059</td>\n",
       "      <td>34.23</td>\n",
       "      <td>-119.04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8210</th>\n",
       "      <td>1.2746</td>\n",
       "      <td>36.0</td>\n",
       "      <td>3.910138</td>\n",
       "      <td>1.267281</td>\n",
       "      <td>1379.0</td>\n",
       "      <td>3.177419</td>\n",
       "      <td>33.78</td>\n",
       "      <td>-118.18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11400</th>\n",
       "      <td>6.7919</td>\n",
       "      <td>19.0</td>\n",
       "      <td>7.377982</td>\n",
       "      <td>1.022018</td>\n",
       "      <td>1872.0</td>\n",
       "      <td>3.434862</td>\n",
       "      <td>33.73</td>\n",
       "      <td>-117.93</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8623</th>\n",
       "      <td>4.8611</td>\n",
       "      <td>39.0</td>\n",
       "      <td>5.514815</td>\n",
       "      <td>1.044444</td>\n",
       "      <td>743.0</td>\n",
       "      <td>2.751852</td>\n",
       "      <td>33.88</td>\n",
       "      <td>-118.38</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10268</th>\n",
       "      <td>3.2083</td>\n",
       "      <td>21.0</td>\n",
       "      <td>4.150273</td>\n",
       "      <td>1.060109</td>\n",
       "      <td>1203.0</td>\n",
       "      <td>3.286885</td>\n",
       "      <td>33.87</td>\n",
       "      <td>-117.88</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6073</th>\n",
       "      <td>11.1077</td>\n",
       "      <td>32.0</td>\n",
       "      <td>8.010526</td>\n",
       "      <td>1.063158</td>\n",
       "      <td>295.0</td>\n",
       "      <td>3.105263</td>\n",
       "      <td>34.07</td>\n",
       "      <td>-117.85</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9677</th>\n",
       "      <td>4.5375</td>\n",
       "      <td>18.0</td>\n",
       "      <td>8.629808</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>483.0</td>\n",
       "      <td>2.322115</td>\n",
       "      <td>37.65</td>\n",
       "      <td>-118.98</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8895</th>\n",
       "      <td>3.6769</td>\n",
       "      <td>31.0</td>\n",
       "      <td>4.234899</td>\n",
       "      <td>1.084564</td>\n",
       "      <td>1208.0</td>\n",
       "      <td>1.621477</td>\n",
       "      <td>34.03</td>\n",
       "      <td>-118.49</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7685</th>\n",
       "      <td>3.1607</td>\n",
       "      <td>33.0</td>\n",
       "      <td>5.415254</td>\n",
       "      <td>1.093220</td>\n",
       "      <td>460.0</td>\n",
       "      <td>3.898305</td>\n",
       "      <td>33.94</td>\n",
       "      <td>-118.10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19833</th>\n",
       "      <td>1.9602</td>\n",
       "      <td>38.0</td>\n",
       "      <td>4.372014</td>\n",
       "      <td>1.013652</td>\n",
       "      <td>1423.0</td>\n",
       "      <td>4.856655</td>\n",
       "      <td>36.53</td>\n",
       "      <td>-119.38</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19842</th>\n",
       "      <td>1.8801</td>\n",
       "      <td>26.0</td>\n",
       "      <td>4.634465</td>\n",
       "      <td>1.086162</td>\n",
       "      <td>1217.0</td>\n",
       "      <td>3.177546</td>\n",
       "      <td>36.42</td>\n",
       "      <td>-119.10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20319</th>\n",
       "      <td>3.6500</td>\n",
       "      <td>15.0</td>\n",
       "      <td>4.679577</td>\n",
       "      <td>0.992958</td>\n",
       "      <td>1001.0</td>\n",
       "      <td>3.524648</td>\n",
       "      <td>34.25</td>\n",
       "      <td>-119.17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4639</th>\n",
       "      <td>1.4680</td>\n",
       "      <td>47.0</td>\n",
       "      <td>1.680774</td>\n",
       "      <td>1.054414</td>\n",
       "      <td>2860.0</td>\n",
       "      <td>3.458283</td>\n",
       "      <td>34.06</td>\n",
       "      <td>-118.30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13304</th>\n",
       "      <td>2.8977</td>\n",
       "      <td>19.0</td>\n",
       "      <td>5.072674</td>\n",
       "      <td>1.186047</td>\n",
       "      <td>2818.0</td>\n",
       "      <td>4.095930</td>\n",
       "      <td>34.09</td>\n",
       "      <td>-117.63</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>197</th>\n",
       "      <td>2.5900</td>\n",
       "      <td>52.0</td>\n",
       "      <td>4.389961</td>\n",
       "      <td>1.162162</td>\n",
       "      <td>866.0</td>\n",
       "      <td>3.343629</td>\n",
       "      <td>37.77</td>\n",
       "      <td>-122.22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12837</th>\n",
       "      <td>5.0155</td>\n",
       "      <td>19.0</td>\n",
       "      <td>5.468208</td>\n",
       "      <td>1.052023</td>\n",
       "      <td>474.0</td>\n",
       "      <td>2.739884</td>\n",
       "      <td>38.68</td>\n",
       "      <td>-121.47</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6592</th>\n",
       "      <td>15.0001</td>\n",
       "      <td>38.0</td>\n",
       "      <td>8.954733</td>\n",
       "      <td>1.094650</td>\n",
       "      <td>798.0</td>\n",
       "      <td>3.283951</td>\n",
       "      <td>34.19</td>\n",
       "      <td>-118.20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3254</th>\n",
       "      <td>2.0335</td>\n",
       "      <td>33.0</td>\n",
       "      <td>4.448454</td>\n",
       "      <td>1.082474</td>\n",
       "      <td>1371.0</td>\n",
       "      <td>3.533505</td>\n",
       "      <td>36.00</td>\n",
       "      <td>-120.14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11093</th>\n",
       "      <td>5.3812</td>\n",
       "      <td>10.0</td>\n",
       "      <td>5.316038</td>\n",
       "      <td>1.146226</td>\n",
       "      <td>1584.0</td>\n",
       "      <td>2.490566</td>\n",
       "      <td>33.84</td>\n",
       "      <td>-117.87</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>152</th>\n",
       "      <td>3.1603</td>\n",
       "      <td>52.0</td>\n",
       "      <td>4.429194</td>\n",
       "      <td>1.058824</td>\n",
       "      <td>787.0</td>\n",
       "      <td>1.714597</td>\n",
       "      <td>37.80</td>\n",
       "      <td>-122.23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>803</th>\n",
       "      <td>3.5541</td>\n",
       "      <td>20.0</td>\n",
       "      <td>3.848837</td>\n",
       "      <td>1.046512</td>\n",
       "      <td>857.0</td>\n",
       "      <td>1.993023</td>\n",
       "      <td>37.64</td>\n",
       "      <td>-122.06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17108</th>\n",
       "      <td>15.0001</td>\n",
       "      <td>32.0</td>\n",
       "      <td>8.845041</td>\n",
       "      <td>1.035124</td>\n",
       "      <td>1318.0</td>\n",
       "      <td>2.723140</td>\n",
       "      <td>37.44</td>\n",
       "      <td>-122.22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9446</th>\n",
       "      <td>2.5000</td>\n",
       "      <td>17.0</td>\n",
       "      <td>6.104019</td>\n",
       "      <td>1.307329</td>\n",
       "      <td>1087.0</td>\n",
       "      <td>2.569740</td>\n",
       "      <td>37.48</td>\n",
       "      <td>-119.84</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18062</th>\n",
       "      <td>7.9234</td>\n",
       "      <td>29.0</td>\n",
       "      <td>8.017699</td>\n",
       "      <td>1.076696</td>\n",
       "      <td>982.0</td>\n",
       "      <td>2.896755</td>\n",
       "      <td>37.26</td>\n",
       "      <td>-121.99</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10903</th>\n",
       "      <td>1.9309</td>\n",
       "      <td>31.0</td>\n",
       "      <td>3.762821</td>\n",
       "      <td>1.100427</td>\n",
       "      <td>1810.0</td>\n",
       "      <td>3.867521</td>\n",
       "      <td>33.75</td>\n",
       "      <td>-117.86</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20094</th>\n",
       "      <td>2.1250</td>\n",
       "      <td>26.0</td>\n",
       "      <td>37.063492</td>\n",
       "      <td>7.185185</td>\n",
       "      <td>416.0</td>\n",
       "      <td>2.201058</td>\n",
       "      <td>38.19</td>\n",
       "      <td>-120.03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>266</th>\n",
       "      <td>3.3715</td>\n",
       "      <td>52.0</td>\n",
       "      <td>5.105882</td>\n",
       "      <td>1.007059</td>\n",
       "      <td>1086.0</td>\n",
       "      <td>2.555294</td>\n",
       "      <td>37.77</td>\n",
       "      <td>-122.19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10394</th>\n",
       "      <td>4.7981</td>\n",
       "      <td>16.0</td>\n",
       "      <td>6.408537</td>\n",
       "      <td>1.067073</td>\n",
       "      <td>1003.0</td>\n",
       "      <td>3.057927</td>\n",
       "      <td>33.54</td>\n",
       "      <td>-117.67</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1209</th>\n",
       "      <td>2.3816</td>\n",
       "      <td>16.0</td>\n",
       "      <td>6.055954</td>\n",
       "      <td>1.120516</td>\n",
       "      <td>1516.0</td>\n",
       "      <td>2.175036</td>\n",
       "      <td>38.15</td>\n",
       "      <td>-120.46</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14254</th>\n",
       "      <td>1.7500</td>\n",
       "      <td>25.0</td>\n",
       "      <td>3.912500</td>\n",
       "      <td>1.029167</td>\n",
       "      <td>1003.0</td>\n",
       "      <td>4.179167</td>\n",
       "      <td>32.71</td>\n",
       "      <td>-117.10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19059</th>\n",
       "      <td>3.5647</td>\n",
       "      <td>33.0</td>\n",
       "      <td>5.423358</td>\n",
       "      <td>1.058394</td>\n",
       "      <td>781.0</td>\n",
       "      <td>2.850365</td>\n",
       "      <td>38.22</td>\n",
       "      <td>-122.49</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11185</th>\n",
       "      <td>5.0599</td>\n",
       "      <td>35.0</td>\n",
       "      <td>5.271429</td>\n",
       "      <td>0.985714</td>\n",
       "      <td>641.0</td>\n",
       "      <td>3.052381</td>\n",
       "      <td>33.83</td>\n",
       "      <td>-117.95</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>14448 rows × 8 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        MedInc  HouseAge   AveRooms  AveBedrms  Population  AveOccup  \\\n",
       "17073   4.1776      35.0   4.425172   1.030683      5380.0  3.368817   \n",
       "16956   5.3261      38.0   6.267516   1.089172       429.0  2.732484   \n",
       "20012   1.9439      26.0   5.768977   1.141914       891.0  2.940594   \n",
       "13072   2.5000      22.0   4.916000   1.012000       733.0  2.932000   \n",
       "8457    3.8250      34.0   5.036765   1.098039      1134.0  2.779412   \n",
       "12684   2.9250      19.0   5.649321   1.117647       748.0  1.692308   \n",
       "17007   5.0081      39.0   5.013746   0.979381       761.0  2.615120   \n",
       "2241    5.6445      10.0   6.545866   1.044168      2712.0  3.071348   \n",
       "7360    2.0162      28.0   3.615464   0.998969      3740.0  3.855670   \n",
       "19676   2.4183       7.0   4.582927   1.082927      1065.0  2.597561   \n",
       "13679   5.3366       4.0   6.083688   0.967376      1981.0  2.809929   \n",
       "19253   4.7450      28.0   6.313933   1.077601      1634.0  2.881834   \n",
       "11126   3.0250      22.0   4.006803   0.982993       865.0  2.942177   \n",
       "2060    2.9013      19.0   6.090909   1.107656      1257.0  3.007177   \n",
       "12094   5.4625       9.0   6.999071   1.044568      3450.0  3.203343   \n",
       "19832   1.5223      33.0   4.722222   1.026820      2030.0  3.888889   \n",
       "15418   3.9815      26.0   6.072508   1.048338      1160.0  3.504532   \n",
       "4891    2.1957      33.0   3.254369   1.019417      2564.0  4.978641   \n",
       "14605   4.2200      33.0   6.278689   1.036885      1355.0  2.776639   \n",
       "20315   3.6167      17.0   3.154930   0.985915       147.0  2.070423   \n",
       "10296   6.3303      14.0   6.028358   0.998507      1905.0  2.843284   \n",
       "3385    5.6184      29.0   5.885417   1.006944      1543.0  2.678819   \n",
       "12241   1.9420      31.0   4.311606   0.998410      1519.0  2.414944   \n",
       "7174    2.7432      35.0   4.695652   1.112319      1423.0  5.155797   \n",
       "17431   1.9257      27.0   4.203036   1.096774      1544.0  2.929791   \n",
       "8984    5.9641      44.0   4.357143   1.028571       156.0  2.228571   \n",
       "1990    1.7011      45.0   4.057018   1.013158       797.0  3.495614   \n",
       "306     1.7056      49.0   5.085502   1.048327       790.0  2.936803   \n",
       "20342   5.0145      21.0   6.310811   1.038610      4199.0  2.702059   \n",
       "8210    1.2746      36.0   3.910138   1.267281      1379.0  3.177419   \n",
       "...        ...       ...        ...        ...         ...       ...   \n",
       "11400   6.7919      19.0   7.377982   1.022018      1872.0  3.434862   \n",
       "8623    4.8611      39.0   5.514815   1.044444       743.0  2.751852   \n",
       "10268   3.2083      21.0   4.150273   1.060109      1203.0  3.286885   \n",
       "6073   11.1077      32.0   8.010526   1.063158       295.0  3.105263   \n",
       "9677    4.5375      18.0   8.629808   2.000000       483.0  2.322115   \n",
       "8895    3.6769      31.0   4.234899   1.084564      1208.0  1.621477   \n",
       "7685    3.1607      33.0   5.415254   1.093220       460.0  3.898305   \n",
       "19833   1.9602      38.0   4.372014   1.013652      1423.0  4.856655   \n",
       "19842   1.8801      26.0   4.634465   1.086162      1217.0  3.177546   \n",
       "20319   3.6500      15.0   4.679577   0.992958      1001.0  3.524648   \n",
       "4639    1.4680      47.0   1.680774   1.054414      2860.0  3.458283   \n",
       "13304   2.8977      19.0   5.072674   1.186047      2818.0  4.095930   \n",
       "197     2.5900      52.0   4.389961   1.162162       866.0  3.343629   \n",
       "12837   5.0155      19.0   5.468208   1.052023       474.0  2.739884   \n",
       "6592   15.0001      38.0   8.954733   1.094650       798.0  3.283951   \n",
       "3254    2.0335      33.0   4.448454   1.082474      1371.0  3.533505   \n",
       "11093   5.3812      10.0   5.316038   1.146226      1584.0  2.490566   \n",
       "152     3.1603      52.0   4.429194   1.058824       787.0  1.714597   \n",
       "803     3.5541      20.0   3.848837   1.046512       857.0  1.993023   \n",
       "17108  15.0001      32.0   8.845041   1.035124      1318.0  2.723140   \n",
       "9446    2.5000      17.0   6.104019   1.307329      1087.0  2.569740   \n",
       "18062   7.9234      29.0   8.017699   1.076696       982.0  2.896755   \n",
       "10903   1.9309      31.0   3.762821   1.100427      1810.0  3.867521   \n",
       "20094   2.1250      26.0  37.063492   7.185185       416.0  2.201058   \n",
       "266     3.3715      52.0   5.105882   1.007059      1086.0  2.555294   \n",
       "10394   4.7981      16.0   6.408537   1.067073      1003.0  3.057927   \n",
       "1209    2.3816      16.0   6.055954   1.120516      1516.0  2.175036   \n",
       "14254   1.7500      25.0   3.912500   1.029167      1003.0  4.179167   \n",
       "19059   3.5647      33.0   5.423358   1.058394       781.0  2.850365   \n",
       "11185   5.0599      35.0   5.271429   0.985714       641.0  3.052381   \n",
       "\n",
       "       Latitude  Longitude  \n",
       "17073     37.48    -122.19  \n",
       "16956     37.53    -122.30  \n",
       "20012     36.02    -119.08  \n",
       "13072     38.57    -121.31  \n",
       "8457      33.91    -118.35  \n",
       "12684     38.55    -121.40  \n",
       "17007     37.54    -122.29  \n",
       "2241      36.83    -119.81  \n",
       "7360      33.96    -118.19  \n",
       "19676     39.16    -121.63  \n",
       "13679     34.04    -117.24  \n",
       "19253     38.46    -122.81  \n",
       "11126     33.85    -117.91  \n",
       "2060      36.65    -119.74  \n",
       "12094     33.85    -117.40  \n",
       "19832     36.54    -119.38  \n",
       "15418     33.22    -117.25  \n",
       "4891      34.02    -118.25  \n",
       "14605     32.81    -117.17  \n",
       "20315     34.12    -119.16  \n",
       "10296     33.92    -117.87  \n",
       "3385      34.24    -118.28  \n",
       "12241     33.74    -116.97  \n",
       "7174      34.05    -118.19  \n",
       "17431     34.65    -120.45  \n",
       "8984      33.99    -118.44  \n",
       "1990      36.73    -119.80  \n",
       "306       37.76    -122.19  \n",
       "20342     34.23    -119.04  \n",
       "8210      33.78    -118.18  \n",
       "...         ...        ...  \n",
       "11400     33.73    -117.93  \n",
       "8623      33.88    -118.38  \n",
       "10268     33.87    -117.88  \n",
       "6073      34.07    -117.85  \n",
       "9677      37.65    -118.98  \n",
       "8895      34.03    -118.49  \n",
       "7685      33.94    -118.10  \n",
       "19833     36.53    -119.38  \n",
       "19842     36.42    -119.10  \n",
       "20319     34.25    -119.17  \n",
       "4639      34.06    -118.30  \n",
       "13304     34.09    -117.63  \n",
       "197       37.77    -122.22  \n",
       "12837     38.68    -121.47  \n",
       "6592      34.19    -118.20  \n",
       "3254      36.00    -120.14  \n",
       "11093     33.84    -117.87  \n",
       "152       37.80    -122.23  \n",
       "803       37.64    -122.06  \n",
       "17108     37.44    -122.22  \n",
       "9446      37.48    -119.84  \n",
       "18062     37.26    -121.99  \n",
       "10903     33.75    -117.86  \n",
       "20094     38.19    -120.03  \n",
       "266       37.77    -122.19  \n",
       "10394     33.54    -117.67  \n",
       "1209      38.15    -120.46  \n",
       "14254     32.71    -117.10  \n",
       "19059     38.22    -122.49  \n",
       "11185     33.83    -117.95  \n",
       "\n",
       "[14448 rows x 8 columns]"
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Xtrain"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "id": "provincial-gnome",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LinearRegression(copy_X=True, fit_intercept=True, n_jobs=None, normalize=False)"
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lr3 = LinearRegression()\n",
    "lr3.fit(X_train_std,Ytrain)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "id": "cooperative-twist",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.6067440341875014"
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lr3.score(X_train_std,Ytrain)\n",
    "#解读：拟合效果与标准化之前的差异不是特别大。"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "promising-mattress",
   "metadata": {},
   "source": [
    "### 绘制拟合图像"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "id": "seasonal-plant",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x1be305c1c88>"
      ]
     },
     "execution_count": 83,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(range(len(Ytest)),Ytest,s =2)\n",
    "#因为数据无序，所以画的点是乱的"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "id": "sharing-evanescence",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x1be30652cc0>"
      ]
     },
     "execution_count": 84,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(range(len(Ytest)),sorted(Ytest),s =2)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "attended-elephant",
   "metadata": {},
   "source": [
    "#预测值与真实值的对应关系，如何匹配？"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "id": "streaming-intermediate",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1.51384887, 0.46566247, 2.2567733 , ..., 2.11885803, 1.76968187,\n",
       "       0.73219077])"
      ]
     },
     "execution_count": 85,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_test_pred"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "id": "variable-ghost",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([2477, 4318, 4930, ..., 2806, 2786, 3736], dtype=int64)"
      ]
     },
     "execution_count": 86,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.argsort(Ytest)#得到排序之后的索引序列"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "id": "blind-allergy",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1.05399796, 0.19034538, 1.92338573, ..., 3.43677069, 2.23272962,\n",
       "       5.19172028])"
      ]
     },
     "execution_count": 87,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_test_pred[np.argsort(Ytest)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "id": "otherwise-politics",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x1be2effbf60>"
      ]
     },
     "execution_count": 90,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(range(len(Ytest)),sorted(Ytest),s =2,label='True')\n",
    "plt.scatter(range(len(Ytest)),y_test_pred[np.argsort(Ytest)],s =2,label='Pred',alpha=0.3)\n",
    "plt.legend()\n",
    "#解读:前面拟合还可以，新预测数据发生偏离的可能性较大，有可能过拟合"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "variable-parker",
   "metadata": {},
   "source": [
    "### 多重共线性"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "id": "golden-edward",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>MedInc</th>\n",
       "      <th>HouseAge</th>\n",
       "      <th>AveRooms</th>\n",
       "      <th>AveBedrms</th>\n",
       "      <th>Population</th>\n",
       "      <th>AveOccup</th>\n",
       "      <th>Latitude</th>\n",
       "      <th>Longitude</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>8.3252</td>\n",
       "      <td>41.0</td>\n",
       "      <td>6.984127</td>\n",
       "      <td>1.023810</td>\n",
       "      <td>322.0</td>\n",
       "      <td>2.555556</td>\n",
       "      <td>37.88</td>\n",
       "      <td>-122.23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>8.3014</td>\n",
       "      <td>21.0</td>\n",
       "      <td>6.238137</td>\n",
       "      <td>0.971880</td>\n",
       "      <td>2401.0</td>\n",
       "      <td>2.109842</td>\n",
       "      <td>37.86</td>\n",
       "      <td>-122.22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>7.2574</td>\n",
       "      <td>52.0</td>\n",
       "      <td>8.288136</td>\n",
       "      <td>1.073446</td>\n",
       "      <td>496.0</td>\n",
       "      <td>2.802260</td>\n",
       "      <td>37.85</td>\n",
       "      <td>-122.24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>5.6431</td>\n",
       "      <td>52.0</td>\n",
       "      <td>5.817352</td>\n",
       "      <td>1.073059</td>\n",
       "      <td>558.0</td>\n",
       "      <td>2.547945</td>\n",
       "      <td>37.85</td>\n",
       "      <td>-122.25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>3.8462</td>\n",
       "      <td>52.0</td>\n",
       "      <td>6.281853</td>\n",
       "      <td>1.081081</td>\n",
       "      <td>565.0</td>\n",
       "      <td>2.181467</td>\n",
       "      <td>37.85</td>\n",
       "      <td>-122.25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>4.0368</td>\n",
       "      <td>52.0</td>\n",
       "      <td>4.761658</td>\n",
       "      <td>1.103627</td>\n",
       "      <td>413.0</td>\n",
       "      <td>2.139896</td>\n",
       "      <td>37.85</td>\n",
       "      <td>-122.25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>3.6591</td>\n",
       "      <td>52.0</td>\n",
       "      <td>4.931907</td>\n",
       "      <td>0.951362</td>\n",
       "      <td>1094.0</td>\n",
       "      <td>2.128405</td>\n",
       "      <td>37.84</td>\n",
       "      <td>-122.25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>3.1200</td>\n",
       "      <td>52.0</td>\n",
       "      <td>4.797527</td>\n",
       "      <td>1.061824</td>\n",
       "      <td>1157.0</td>\n",
       "      <td>1.788253</td>\n",
       "      <td>37.84</td>\n",
       "      <td>-122.25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>2.0804</td>\n",
       "      <td>42.0</td>\n",
       "      <td>4.294118</td>\n",
       "      <td>1.117647</td>\n",
       "      <td>1206.0</td>\n",
       "      <td>2.026891</td>\n",
       "      <td>37.84</td>\n",
       "      <td>-122.26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>3.6912</td>\n",
       "      <td>52.0</td>\n",
       "      <td>4.970588</td>\n",
       "      <td>0.990196</td>\n",
       "      <td>1551.0</td>\n",
       "      <td>2.172269</td>\n",
       "      <td>37.84</td>\n",
       "      <td>-122.25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>3.2031</td>\n",
       "      <td>52.0</td>\n",
       "      <td>5.477612</td>\n",
       "      <td>1.079602</td>\n",
       "      <td>910.0</td>\n",
       "      <td>2.263682</td>\n",
       "      <td>37.85</td>\n",
       "      <td>-122.26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>3.2705</td>\n",
       "      <td>52.0</td>\n",
       "      <td>4.772480</td>\n",
       "      <td>1.024523</td>\n",
       "      <td>1504.0</td>\n",
       "      <td>2.049046</td>\n",
       "      <td>37.85</td>\n",
       "      <td>-122.26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>3.0750</td>\n",
       "      <td>52.0</td>\n",
       "      <td>5.322650</td>\n",
       "      <td>1.012821</td>\n",
       "      <td>1098.0</td>\n",
       "      <td>2.346154</td>\n",
       "      <td>37.85</td>\n",
       "      <td>-122.26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>2.6736</td>\n",
       "      <td>52.0</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>1.097701</td>\n",
       "      <td>345.0</td>\n",
       "      <td>1.982759</td>\n",
       "      <td>37.84</td>\n",
       "      <td>-122.26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>1.9167</td>\n",
       "      <td>52.0</td>\n",
       "      <td>4.262903</td>\n",
       "      <td>1.009677</td>\n",
       "      <td>1212.0</td>\n",
       "      <td>1.954839</td>\n",
       "      <td>37.85</td>\n",
       "      <td>-122.26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>2.1250</td>\n",
       "      <td>50.0</td>\n",
       "      <td>4.242424</td>\n",
       "      <td>1.071970</td>\n",
       "      <td>697.0</td>\n",
       "      <td>2.640152</td>\n",
       "      <td>37.85</td>\n",
       "      <td>-122.26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>2.7750</td>\n",
       "      <td>52.0</td>\n",
       "      <td>5.939577</td>\n",
       "      <td>1.048338</td>\n",
       "      <td>793.0</td>\n",
       "      <td>2.395770</td>\n",
       "      <td>37.85</td>\n",
       "      <td>-122.27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>2.1202</td>\n",
       "      <td>52.0</td>\n",
       "      <td>4.052805</td>\n",
       "      <td>0.966997</td>\n",
       "      <td>648.0</td>\n",
       "      <td>2.138614</td>\n",
       "      <td>37.85</td>\n",
       "      <td>-122.27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>1.9911</td>\n",
       "      <td>50.0</td>\n",
       "      <td>5.343675</td>\n",
       "      <td>1.085919</td>\n",
       "      <td>990.0</td>\n",
       "      <td>2.362768</td>\n",
       "      <td>37.84</td>\n",
       "      <td>-122.26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>2.6033</td>\n",
       "      <td>52.0</td>\n",
       "      <td>5.465455</td>\n",
       "      <td>1.083636</td>\n",
       "      <td>690.0</td>\n",
       "      <td>2.509091</td>\n",
       "      <td>37.84</td>\n",
       "      <td>-122.27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>1.3578</td>\n",
       "      <td>40.0</td>\n",
       "      <td>4.524096</td>\n",
       "      <td>1.108434</td>\n",
       "      <td>409.0</td>\n",
       "      <td>2.463855</td>\n",
       "      <td>37.85</td>\n",
       "      <td>-122.27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>1.7135</td>\n",
       "      <td>42.0</td>\n",
       "      <td>4.478142</td>\n",
       "      <td>1.002732</td>\n",
       "      <td>929.0</td>\n",
       "      <td>2.538251</td>\n",
       "      <td>37.85</td>\n",
       "      <td>-122.27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>1.7250</td>\n",
       "      <td>52.0</td>\n",
       "      <td>5.096234</td>\n",
       "      <td>1.131799</td>\n",
       "      <td>1015.0</td>\n",
       "      <td>2.123431</td>\n",
       "      <td>37.84</td>\n",
       "      <td>-122.27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>2.1806</td>\n",
       "      <td>52.0</td>\n",
       "      <td>5.193846</td>\n",
       "      <td>1.036923</td>\n",
       "      <td>853.0</td>\n",
       "      <td>2.624615</td>\n",
       "      <td>37.84</td>\n",
       "      <td>-122.27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>2.6000</td>\n",
       "      <td>52.0</td>\n",
       "      <td>5.270142</td>\n",
       "      <td>1.035545</td>\n",
       "      <td>1006.0</td>\n",
       "      <td>2.383886</td>\n",
       "      <td>37.84</td>\n",
       "      <td>-122.27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>2.4038</td>\n",
       "      <td>41.0</td>\n",
       "      <td>4.495798</td>\n",
       "      <td>1.033613</td>\n",
       "      <td>317.0</td>\n",
       "      <td>2.663866</td>\n",
       "      <td>37.85</td>\n",
       "      <td>-122.28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>2.4597</td>\n",
       "      <td>49.0</td>\n",
       "      <td>4.728033</td>\n",
       "      <td>1.020921</td>\n",
       "      <td>607.0</td>\n",
       "      <td>2.539749</td>\n",
       "      <td>37.85</td>\n",
       "      <td>-122.28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>1.8080</td>\n",
       "      <td>52.0</td>\n",
       "      <td>4.780856</td>\n",
       "      <td>1.060453</td>\n",
       "      <td>1102.0</td>\n",
       "      <td>2.775819</td>\n",
       "      <td>37.85</td>\n",
       "      <td>-122.28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>1.6424</td>\n",
       "      <td>50.0</td>\n",
       "      <td>4.401691</td>\n",
       "      <td>1.040169</td>\n",
       "      <td>1131.0</td>\n",
       "      <td>2.391121</td>\n",
       "      <td>37.84</td>\n",
       "      <td>-122.28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>1.6875</td>\n",
       "      <td>52.0</td>\n",
       "      <td>4.703226</td>\n",
       "      <td>1.032258</td>\n",
       "      <td>395.0</td>\n",
       "      <td>2.548387</td>\n",
       "      <td>37.84</td>\n",
       "      <td>-122.28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20610</th>\n",
       "      <td>1.3631</td>\n",
       "      <td>28.0</td>\n",
       "      <td>4.851936</td>\n",
       "      <td>1.102506</td>\n",
       "      <td>1195.0</td>\n",
       "      <td>2.722096</td>\n",
       "      <td>39.10</td>\n",
       "      <td>-121.56</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20611</th>\n",
       "      <td>1.2857</td>\n",
       "      <td>27.0</td>\n",
       "      <td>4.359413</td>\n",
       "      <td>1.078240</td>\n",
       "      <td>1163.0</td>\n",
       "      <td>2.843521</td>\n",
       "      <td>39.10</td>\n",
       "      <td>-121.55</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20612</th>\n",
       "      <td>1.4934</td>\n",
       "      <td>26.0</td>\n",
       "      <td>5.157303</td>\n",
       "      <td>1.082397</td>\n",
       "      <td>761.0</td>\n",
       "      <td>2.850187</td>\n",
       "      <td>39.08</td>\n",
       "      <td>-121.56</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20613</th>\n",
       "      <td>1.4958</td>\n",
       "      <td>31.0</td>\n",
       "      <td>4.500000</td>\n",
       "      <td>0.950521</td>\n",
       "      <td>1167.0</td>\n",
       "      <td>3.039062</td>\n",
       "      <td>39.09</td>\n",
       "      <td>-121.55</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20614</th>\n",
       "      <td>2.4695</td>\n",
       "      <td>26.0</td>\n",
       "      <td>4.801688</td>\n",
       "      <td>0.970464</td>\n",
       "      <td>1455.0</td>\n",
       "      <td>3.069620</td>\n",
       "      <td>39.08</td>\n",
       "      <td>-121.54</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20615</th>\n",
       "      <td>2.3598</td>\n",
       "      <td>23.0</td>\n",
       "      <td>5.461929</td>\n",
       "      <td>1.096447</td>\n",
       "      <td>724.0</td>\n",
       "      <td>3.675127</td>\n",
       "      <td>39.08</td>\n",
       "      <td>-121.54</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20616</th>\n",
       "      <td>2.0469</td>\n",
       "      <td>15.0</td>\n",
       "      <td>4.826667</td>\n",
       "      <td>1.176000</td>\n",
       "      <td>1157.0</td>\n",
       "      <td>3.085333</td>\n",
       "      <td>39.08</td>\n",
       "      <td>-121.53</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20617</th>\n",
       "      <td>3.3021</td>\n",
       "      <td>20.0</td>\n",
       "      <td>4.921053</td>\n",
       "      <td>0.956140</td>\n",
       "      <td>308.0</td>\n",
       "      <td>2.701754</td>\n",
       "      <td>39.06</td>\n",
       "      <td>-121.53</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20618</th>\n",
       "      <td>2.2500</td>\n",
       "      <td>25.0</td>\n",
       "      <td>5.893805</td>\n",
       "      <td>1.092920</td>\n",
       "      <td>726.0</td>\n",
       "      <td>3.212389</td>\n",
       "      <td>39.06</td>\n",
       "      <td>-121.55</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20619</th>\n",
       "      <td>2.7303</td>\n",
       "      <td>22.0</td>\n",
       "      <td>6.388514</td>\n",
       "      <td>1.148649</td>\n",
       "      <td>1023.0</td>\n",
       "      <td>3.456081</td>\n",
       "      <td>39.01</td>\n",
       "      <td>-121.56</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20620</th>\n",
       "      <td>4.5625</td>\n",
       "      <td>40.0</td>\n",
       "      <td>4.125000</td>\n",
       "      <td>0.854167</td>\n",
       "      <td>151.0</td>\n",
       "      <td>3.145833</td>\n",
       "      <td>39.05</td>\n",
       "      <td>-121.48</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20621</th>\n",
       "      <td>2.3661</td>\n",
       "      <td>37.0</td>\n",
       "      <td>7.923567</td>\n",
       "      <td>1.573248</td>\n",
       "      <td>484.0</td>\n",
       "      <td>3.082803</td>\n",
       "      <td>39.01</td>\n",
       "      <td>-121.47</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20622</th>\n",
       "      <td>2.4167</td>\n",
       "      <td>20.0</td>\n",
       "      <td>4.808917</td>\n",
       "      <td>0.936306</td>\n",
       "      <td>457.0</td>\n",
       "      <td>2.910828</td>\n",
       "      <td>39.00</td>\n",
       "      <td>-121.44</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20623</th>\n",
       "      <td>2.8235</td>\n",
       "      <td>32.0</td>\n",
       "      <td>5.101322</td>\n",
       "      <td>1.074890</td>\n",
       "      <td>598.0</td>\n",
       "      <td>2.634361</td>\n",
       "      <td>39.03</td>\n",
       "      <td>-121.37</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20624</th>\n",
       "      <td>3.0739</td>\n",
       "      <td>16.0</td>\n",
       "      <td>5.835052</td>\n",
       "      <td>1.030928</td>\n",
       "      <td>731.0</td>\n",
       "      <td>2.512027</td>\n",
       "      <td>39.04</td>\n",
       "      <td>-121.41</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20625</th>\n",
       "      <td>4.1250</td>\n",
       "      <td>37.0</td>\n",
       "      <td>7.285714</td>\n",
       "      <td>1.214286</td>\n",
       "      <td>29.0</td>\n",
       "      <td>2.071429</td>\n",
       "      <td>39.12</td>\n",
       "      <td>-121.52</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20626</th>\n",
       "      <td>2.1667</td>\n",
       "      <td>36.0</td>\n",
       "      <td>6.573099</td>\n",
       "      <td>1.076023</td>\n",
       "      <td>504.0</td>\n",
       "      <td>2.947368</td>\n",
       "      <td>39.18</td>\n",
       "      <td>-121.43</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20627</th>\n",
       "      <td>3.0000</td>\n",
       "      <td>5.0</td>\n",
       "      <td>6.067797</td>\n",
       "      <td>1.101695</td>\n",
       "      <td>169.0</td>\n",
       "      <td>2.864407</td>\n",
       "      <td>39.13</td>\n",
       "      <td>-121.32</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20628</th>\n",
       "      <td>2.5952</td>\n",
       "      <td>19.0</td>\n",
       "      <td>5.238462</td>\n",
       "      <td>1.079487</td>\n",
       "      <td>1018.0</td>\n",
       "      <td>2.610256</td>\n",
       "      <td>39.10</td>\n",
       "      <td>-121.48</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20629</th>\n",
       "      <td>2.0943</td>\n",
       "      <td>28.0</td>\n",
       "      <td>5.519802</td>\n",
       "      <td>1.020902</td>\n",
       "      <td>6912.0</td>\n",
       "      <td>3.801980</td>\n",
       "      <td>39.12</td>\n",
       "      <td>-121.39</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20630</th>\n",
       "      <td>3.5673</td>\n",
       "      <td>11.0</td>\n",
       "      <td>5.932584</td>\n",
       "      <td>1.134831</td>\n",
       "      <td>1257.0</td>\n",
       "      <td>2.824719</td>\n",
       "      <td>39.29</td>\n",
       "      <td>-121.32</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20631</th>\n",
       "      <td>3.5179</td>\n",
       "      <td>15.0</td>\n",
       "      <td>6.145833</td>\n",
       "      <td>1.141204</td>\n",
       "      <td>1200.0</td>\n",
       "      <td>2.777778</td>\n",
       "      <td>39.33</td>\n",
       "      <td>-121.40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20632</th>\n",
       "      <td>3.1250</td>\n",
       "      <td>15.0</td>\n",
       "      <td>6.023377</td>\n",
       "      <td>1.080519</td>\n",
       "      <td>1047.0</td>\n",
       "      <td>2.719481</td>\n",
       "      <td>39.26</td>\n",
       "      <td>-121.45</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20633</th>\n",
       "      <td>2.5495</td>\n",
       "      <td>27.0</td>\n",
       "      <td>5.445026</td>\n",
       "      <td>1.078534</td>\n",
       "      <td>1082.0</td>\n",
       "      <td>2.832461</td>\n",
       "      <td>39.19</td>\n",
       "      <td>-121.53</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20634</th>\n",
       "      <td>3.7125</td>\n",
       "      <td>28.0</td>\n",
       "      <td>6.779070</td>\n",
       "      <td>1.148256</td>\n",
       "      <td>1041.0</td>\n",
       "      <td>3.026163</td>\n",
       "      <td>39.27</td>\n",
       "      <td>-121.56</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20635</th>\n",
       "      <td>1.5603</td>\n",
       "      <td>25.0</td>\n",
       "      <td>5.045455</td>\n",
       "      <td>1.133333</td>\n",
       "      <td>845.0</td>\n",
       "      <td>2.560606</td>\n",
       "      <td>39.48</td>\n",
       "      <td>-121.09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20636</th>\n",
       "      <td>2.5568</td>\n",
       "      <td>18.0</td>\n",
       "      <td>6.114035</td>\n",
       "      <td>1.315789</td>\n",
       "      <td>356.0</td>\n",
       "      <td>3.122807</td>\n",
       "      <td>39.49</td>\n",
       "      <td>-121.21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20637</th>\n",
       "      <td>1.7000</td>\n",
       "      <td>17.0</td>\n",
       "      <td>5.205543</td>\n",
       "      <td>1.120092</td>\n",
       "      <td>1007.0</td>\n",
       "      <td>2.325635</td>\n",
       "      <td>39.43</td>\n",
       "      <td>-121.22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20638</th>\n",
       "      <td>1.8672</td>\n",
       "      <td>18.0</td>\n",
       "      <td>5.329513</td>\n",
       "      <td>1.171920</td>\n",
       "      <td>741.0</td>\n",
       "      <td>2.123209</td>\n",
       "      <td>39.43</td>\n",
       "      <td>-121.32</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20639</th>\n",
       "      <td>2.3886</td>\n",
       "      <td>16.0</td>\n",
       "      <td>5.254717</td>\n",
       "      <td>1.162264</td>\n",
       "      <td>1387.0</td>\n",
       "      <td>2.616981</td>\n",
       "      <td>39.37</td>\n",
       "      <td>-121.24</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>20640 rows × 8 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       MedInc  HouseAge  AveRooms  AveBedrms  Population  AveOccup  Latitude  \\\n",
       "0      8.3252      41.0  6.984127   1.023810       322.0  2.555556     37.88   \n",
       "1      8.3014      21.0  6.238137   0.971880      2401.0  2.109842     37.86   \n",
       "2      7.2574      52.0  8.288136   1.073446       496.0  2.802260     37.85   \n",
       "3      5.6431      52.0  5.817352   1.073059       558.0  2.547945     37.85   \n",
       "4      3.8462      52.0  6.281853   1.081081       565.0  2.181467     37.85   \n",
       "5      4.0368      52.0  4.761658   1.103627       413.0  2.139896     37.85   \n",
       "6      3.6591      52.0  4.931907   0.951362      1094.0  2.128405     37.84   \n",
       "7      3.1200      52.0  4.797527   1.061824      1157.0  1.788253     37.84   \n",
       "8      2.0804      42.0  4.294118   1.117647      1206.0  2.026891     37.84   \n",
       "9      3.6912      52.0  4.970588   0.990196      1551.0  2.172269     37.84   \n",
       "10     3.2031      52.0  5.477612   1.079602       910.0  2.263682     37.85   \n",
       "11     3.2705      52.0  4.772480   1.024523      1504.0  2.049046     37.85   \n",
       "12     3.0750      52.0  5.322650   1.012821      1098.0  2.346154     37.85   \n",
       "13     2.6736      52.0  4.000000   1.097701       345.0  1.982759     37.84   \n",
       "14     1.9167      52.0  4.262903   1.009677      1212.0  1.954839     37.85   \n",
       "15     2.1250      50.0  4.242424   1.071970       697.0  2.640152     37.85   \n",
       "16     2.7750      52.0  5.939577   1.048338       793.0  2.395770     37.85   \n",
       "17     2.1202      52.0  4.052805   0.966997       648.0  2.138614     37.85   \n",
       "18     1.9911      50.0  5.343675   1.085919       990.0  2.362768     37.84   \n",
       "19     2.6033      52.0  5.465455   1.083636       690.0  2.509091     37.84   \n",
       "20     1.3578      40.0  4.524096   1.108434       409.0  2.463855     37.85   \n",
       "21     1.7135      42.0  4.478142   1.002732       929.0  2.538251     37.85   \n",
       "22     1.7250      52.0  5.096234   1.131799      1015.0  2.123431     37.84   \n",
       "23     2.1806      52.0  5.193846   1.036923       853.0  2.624615     37.84   \n",
       "24     2.6000      52.0  5.270142   1.035545      1006.0  2.383886     37.84   \n",
       "25     2.4038      41.0  4.495798   1.033613       317.0  2.663866     37.85   \n",
       "26     2.4597      49.0  4.728033   1.020921       607.0  2.539749     37.85   \n",
       "27     1.8080      52.0  4.780856   1.060453      1102.0  2.775819     37.85   \n",
       "28     1.6424      50.0  4.401691   1.040169      1131.0  2.391121     37.84   \n",
       "29     1.6875      52.0  4.703226   1.032258       395.0  2.548387     37.84   \n",
       "...       ...       ...       ...        ...         ...       ...       ...   \n",
       "20610  1.3631      28.0  4.851936   1.102506      1195.0  2.722096     39.10   \n",
       "20611  1.2857      27.0  4.359413   1.078240      1163.0  2.843521     39.10   \n",
       "20612  1.4934      26.0  5.157303   1.082397       761.0  2.850187     39.08   \n",
       "20613  1.4958      31.0  4.500000   0.950521      1167.0  3.039062     39.09   \n",
       "20614  2.4695      26.0  4.801688   0.970464      1455.0  3.069620     39.08   \n",
       "20615  2.3598      23.0  5.461929   1.096447       724.0  3.675127     39.08   \n",
       "20616  2.0469      15.0  4.826667   1.176000      1157.0  3.085333     39.08   \n",
       "20617  3.3021      20.0  4.921053   0.956140       308.0  2.701754     39.06   \n",
       "20618  2.2500      25.0  5.893805   1.092920       726.0  3.212389     39.06   \n",
       "20619  2.7303      22.0  6.388514   1.148649      1023.0  3.456081     39.01   \n",
       "20620  4.5625      40.0  4.125000   0.854167       151.0  3.145833     39.05   \n",
       "20621  2.3661      37.0  7.923567   1.573248       484.0  3.082803     39.01   \n",
       "20622  2.4167      20.0  4.808917   0.936306       457.0  2.910828     39.00   \n",
       "20623  2.8235      32.0  5.101322   1.074890       598.0  2.634361     39.03   \n",
       "20624  3.0739      16.0  5.835052   1.030928       731.0  2.512027     39.04   \n",
       "20625  4.1250      37.0  7.285714   1.214286        29.0  2.071429     39.12   \n",
       "20626  2.1667      36.0  6.573099   1.076023       504.0  2.947368     39.18   \n",
       "20627  3.0000       5.0  6.067797   1.101695       169.0  2.864407     39.13   \n",
       "20628  2.5952      19.0  5.238462   1.079487      1018.0  2.610256     39.10   \n",
       "20629  2.0943      28.0  5.519802   1.020902      6912.0  3.801980     39.12   \n",
       "20630  3.5673      11.0  5.932584   1.134831      1257.0  2.824719     39.29   \n",
       "20631  3.5179      15.0  6.145833   1.141204      1200.0  2.777778     39.33   \n",
       "20632  3.1250      15.0  6.023377   1.080519      1047.0  2.719481     39.26   \n",
       "20633  2.5495      27.0  5.445026   1.078534      1082.0  2.832461     39.19   \n",
       "20634  3.7125      28.0  6.779070   1.148256      1041.0  3.026163     39.27   \n",
       "20635  1.5603      25.0  5.045455   1.133333       845.0  2.560606     39.48   \n",
       "20636  2.5568      18.0  6.114035   1.315789       356.0  3.122807     39.49   \n",
       "20637  1.7000      17.0  5.205543   1.120092      1007.0  2.325635     39.43   \n",
       "20638  1.8672      18.0  5.329513   1.171920       741.0  2.123209     39.43   \n",
       "20639  2.3886      16.0  5.254717   1.162264      1387.0  2.616981     39.37   \n",
       "\n",
       "       Longitude  \n",
       "0        -122.23  \n",
       "1        -122.22  \n",
       "2        -122.24  \n",
       "3        -122.25  \n",
       "4        -122.25  \n",
       "5        -122.25  \n",
       "6        -122.25  \n",
       "7        -122.25  \n",
       "8        -122.26  \n",
       "9        -122.25  \n",
       "10       -122.26  \n",
       "11       -122.26  \n",
       "12       -122.26  \n",
       "13       -122.26  \n",
       "14       -122.26  \n",
       "15       -122.26  \n",
       "16       -122.27  \n",
       "17       -122.27  \n",
       "18       -122.26  \n",
       "19       -122.27  \n",
       "20       -122.27  \n",
       "21       -122.27  \n",
       "22       -122.27  \n",
       "23       -122.27  \n",
       "24       -122.27  \n",
       "25       -122.28  \n",
       "26       -122.28  \n",
       "27       -122.28  \n",
       "28       -122.28  \n",
       "29       -122.28  \n",
       "...          ...  \n",
       "20610    -121.56  \n",
       "20611    -121.55  \n",
       "20612    -121.56  \n",
       "20613    -121.55  \n",
       "20614    -121.54  \n",
       "20615    -121.54  \n",
       "20616    -121.53  \n",
       "20617    -121.53  \n",
       "20618    -121.55  \n",
       "20619    -121.56  \n",
       "20620    -121.48  \n",
       "20621    -121.47  \n",
       "20622    -121.44  \n",
       "20623    -121.37  \n",
       "20624    -121.41  \n",
       "20625    -121.52  \n",
       "20626    -121.43  \n",
       "20627    -121.32  \n",
       "20628    -121.48  \n",
       "20629    -121.39  \n",
       "20630    -121.32  \n",
       "20631    -121.40  \n",
       "20632    -121.45  \n",
       "20633    -121.53  \n",
       "20634    -121.56  \n",
       "20635    -121.09  \n",
       "20636    -121.21  \n",
       "20637    -121.22  \n",
       "20638    -121.32  \n",
       "20639    -121.24  \n",
       "\n",
       "[20640 rows x 8 columns]"
      ]
     },
     "execution_count": 91,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "id": "representative-fancy",
   "metadata": {},
   "outputs": [],
   "source": [
    "X.columns= ['该街区住户的收入中位数','该街区房屋使用年代的中位数','该街区平均的房间数目',\n",
    "           '该街区平均的卧室数目','街区人口','平均入住率','街区的纬度','街区的经度']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "id": "conscious-muscle",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import PolynomialFeatures"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "id": "treated-satisfaction",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['1',\n",
       " '该街区住户的收入中位数',\n",
       " '该街区房屋使用年代的中位数',\n",
       " '该街区平均的房间数目',\n",
       " '该街区平均的卧室数目',\n",
       " '街区人口',\n",
       " '平均入住率',\n",
       " '街区的纬度',\n",
       " '街区的经度',\n",
       " '该街区住户的收入中位数^2',\n",
       " '该街区住户的收入中位数 该街区房屋使用年代的中位数',\n",
       " '该街区住户的收入中位数 该街区平均的房间数目',\n",
       " '该街区住户的收入中位数 该街区平均的卧室数目',\n",
       " '该街区住户的收入中位数 街区人口',\n",
       " '该街区住户的收入中位数 平均入住率',\n",
       " '该街区住户的收入中位数 街区的纬度',\n",
       " '该街区住户的收入中位数 街区的经度',\n",
       " '该街区房屋使用年代的中位数^2',\n",
       " '该街区房屋使用年代的中位数 该街区平均的房间数目',\n",
       " '该街区房屋使用年代的中位数 该街区平均的卧室数目',\n",
       " '该街区房屋使用年代的中位数 街区人口',\n",
       " '该街区房屋使用年代的中位数 平均入住率',\n",
       " '该街区房屋使用年代的中位数 街区的纬度',\n",
       " '该街区房屋使用年代的中位数 街区的经度',\n",
       " '该街区平均的房间数目^2',\n",
       " '该街区平均的房间数目 该街区平均的卧室数目',\n",
       " '该街区平均的房间数目 街区人口',\n",
       " '该街区平均的房间数目 平均入住率',\n",
       " '该街区平均的房间数目 街区的纬度',\n",
       " '该街区平均的房间数目 街区的经度',\n",
       " '该街区平均的卧室数目^2',\n",
       " '该街区平均的卧室数目 街区人口',\n",
       " '该街区平均的卧室数目 平均入住率',\n",
       " '该街区平均的卧室数目 街区的纬度',\n",
       " '该街区平均的卧室数目 街区的经度',\n",
       " '街区人口^2',\n",
       " '街区人口 平均入住率',\n",
       " '街区人口 街区的纬度',\n",
       " '街区人口 街区的经度',\n",
       " '平均入住率^2',\n",
       " '平均入住率 街区的纬度',\n",
       " '平均入住率 街区的经度',\n",
       " '街区的纬度^2',\n",
       " '街区的纬度 街区的经度',\n",
       " '街区的经度^2']"
      ]
     },
     "execution_count": 103,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "poly = PolynomialFeatures(degree = 2).fit(X,y)\n",
    "poly.get_feature_names(X.columns)#通过多项式构造列"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "id": "double-christmas",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 1.00000000e+00,  8.32520000e+00,  4.10000000e+01, ...,\n",
       "         1.43489440e+03, -4.63007240e+03,  1.49401729e+04],\n",
       "       [ 1.00000000e+00,  8.30140000e+00,  2.10000000e+01, ...,\n",
       "         1.43337960e+03, -4.62724920e+03,  1.49377284e+04],\n",
       "       [ 1.00000000e+00,  7.25740000e+00,  5.20000000e+01, ...,\n",
       "         1.43262250e+03, -4.62678400e+03,  1.49426176e+04],\n",
       "       ...,\n",
       "       [ 1.00000000e+00,  1.70000000e+00,  1.70000000e+01, ...,\n",
       "         1.55472490e+03, -4.77970460e+03,  1.46942884e+04],\n",
       "       [ 1.00000000e+00,  1.86720000e+00,  1.80000000e+01, ...,\n",
       "         1.55472490e+03, -4.78364760e+03,  1.47185424e+04],\n",
       "       [ 1.00000000e+00,  2.38860000e+00,  1.60000000e+01, ...,\n",
       "         1.54999690e+03, -4.77321880e+03,  1.46991376e+04]])"
      ]
     },
     "execution_count": 104,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_ = poly.transform(X)\n",
    "X_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "id": "naked-jonathan",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 5.91954054e-08, -1.12430252e+01, -8.48898543e-01,  6.44105898e+00,\n",
       "       -3.15913288e+01,  4.06090344e-04,  1.00386234e+00,  8.70568188e+00,\n",
       "        5.88063272e+00, -3.13081272e-02,  1.85994682e-03,  4.33020468e-02,\n",
       "       -1.86142278e-01,  5.72831545e-05, -2.59019509e-03, -1.52505713e-01,\n",
       "       -1.44242939e-01,  2.11725336e-04, -1.26219010e-03,  1.06115056e-02,\n",
       "        2.81885293e-06, -1.81716947e-03, -1.00690372e-02, -9.99950167e-03,\n",
       "        7.26947730e-03, -6.89064340e-02, -6.82365908e-05,  2.68878842e-02,\n",
       "        8.75089875e-02,  8.22890339e-02,  1.60180950e-01,  5.14264271e-04,\n",
       "       -8.71911472e-02, -4.37042992e-01, -4.04150578e-01,  2.73779754e-09,\n",
       "        1.91426762e-05,  2.29529789e-05,  1.46567733e-05,  8.71560978e-05,\n",
       "        2.13344592e-02,  1.62412938e-02,  6.18867358e-02,  1.08107173e-01,\n",
       "        3.99077351e-02])"
      ]
     },
     "execution_count": 105,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "reg = LinearRegression().fit(X_,y)#使用准话后的数据进行模型训练\n",
    "reg.coef_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "id": "proper-johnson",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[('1', 5.919540543535431e-08),\n",
       " ('该街区住户的收入中位数', -11.243025193047133),\n",
       " ('该街区房屋使用年代的中位数', -0.8488985429774577),\n",
       " ('该街区平均的房间数目', 6.441058979309905),\n",
       " ('该街区平均的卧室数目', -31.591328783950864),\n",
       " ('街区人口', 0.0004060903437363997),\n",
       " ('平均入住率', 1.0038623386910916),\n",
       " ('街区的纬度', 8.705681884553798),\n",
       " ('街区的经度', 5.880632723618107),\n",
       " ('该街区住户的收入中位数^2', -0.031308127167766604),\n",
       " ('该街区住户的收入中位数 该街区房屋使用年代的中位数', 0.0018599468180089292),\n",
       " ('该街区住户的收入中位数 该街区平均的房间数目', 0.04330204675617813),\n",
       " ('该街区住户的收入中位数 该街区平均的卧室数目', -0.18614227805444597),\n",
       " ('该街区住户的收入中位数 街区人口', 5.728315446833276e-05),\n",
       " ('该街区住户的收入中位数 平均入住率', -0.0025901950898045813),\n",
       " ('该街区住户的收入中位数 街区的纬度', -0.15250571255257905),\n",
       " ('该街区住户的收入中位数 街区的经度', -0.1442429393710074),\n",
       " ('该街区房屋使用年代的中位数^2', 0.00021172533628449324),\n",
       " ('该街区房屋使用年代的中位数 该街区平均的房间数目', -0.0012621900986623286),\n",
       " ('该街区房屋使用年代的中位数 该街区平均的卧室数目', 0.010611505610669234),\n",
       " ('该街区房屋使用年代的中位数 街区人口', 2.8188529325383913e-06),\n",
       " ('该街区房屋使用年代的中位数 平均入住率', -0.0018171694685867486),\n",
       " ('该街区房屋使用年代的中位数 街区的纬度', -0.01006903715603962),\n",
       " ('该街区房屋使用年代的中位数 街区的经度', -0.00999950167106151),\n",
       " ('该街区平均的房间数目^2', 0.007269477297201673),\n",
       " ('该街区平均的房间数目 该街区平均的卧室数目', -0.06890643403937624),\n",
       " ('该街区平均的房间数目 街区人口', -6.823659076177961e-05),\n",
       " ('该街区平均的房间数目 平均入住率', 0.026887884151996024),\n",
       " ('该街区平均的房间数目 街区的纬度', 0.08750898753013794),\n",
       " ('该街区平均的房间数目 街区的经度', 0.08228903388514937),\n",
       " ('该街区平均的卧室数目^2', 0.16018094998343121),\n",
       " ('该街区平均的卧室数目 街区人口', 0.0005142642707184748),\n",
       " ('该街区平均的卧室数目 平均入住率', -0.08719114716574057),\n",
       " ('该街区平均的卧室数目 街区的纬度', -0.4370429917305055),\n",
       " ('该街区平均的卧室数目 街区的经度', -0.4041505775254418),\n",
       " ('街区人口^2', 2.7377975442277602e-09),\n",
       " ('街区人口 平均入住率', 1.9142676169226283e-05),\n",
       " ('街区人口 街区的纬度', 2.2952978921714218e-05),\n",
       " ('街区人口 街区的经度', 1.4656773312970994e-05),\n",
       " ('平均入住率^2', 8.715609781953472e-05),\n",
       " ('平均入住率 街区的纬度', 0.021334459219532354),\n",
       " ('平均入住率 街区的经度', 0.0162412938291476),\n",
       " ('街区的纬度^2', 0.06188673577349265),\n",
       " ('街区的纬度 街区的经度', 0.10810717324450496),\n",
       " ('街区的经度^2', 0.03990773507988879)]"
      ]
     },
     "execution_count": 107,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "[*zip(poly.get_feature_names(X.columns),reg.coef_)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "id": "crazy-therapy",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>5.91954e-08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>该街区住户的收入中位数</td>\n",
       "      <td>-11.243</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>该街区房屋使用年代的中位数</td>\n",
       "      <td>-0.848899</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>该街区平均的房间数目</td>\n",
       "      <td>6.44106</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>该街区平均的卧室数目</td>\n",
       "      <td>-31.5913</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>街区人口</td>\n",
       "      <td>0.00040609</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>平均入住率</td>\n",
       "      <td>1.00386</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>街区的纬度</td>\n",
       "      <td>8.70568</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>街区的经度</td>\n",
       "      <td>5.88063</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>该街区住户的收入中位数^2</td>\n",
       "      <td>-0.0313081</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>该街区住户的收入中位数 该街区房屋使用年代的中位数</td>\n",
       "      <td>0.00185995</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>该街区住户的收入中位数 该街区平均的房间数目</td>\n",
       "      <td>0.043302</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>该街区住户的收入中位数 该街区平均的卧室数目</td>\n",
       "      <td>-0.186142</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>该街区住户的收入中位数 街区人口</td>\n",
       "      <td>5.72832e-05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>该街区住户的收入中位数 平均入住率</td>\n",
       "      <td>-0.0025902</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>该街区住户的收入中位数 街区的纬度</td>\n",
       "      <td>-0.152506</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>该街区住户的收入中位数 街区的经度</td>\n",
       "      <td>-0.144243</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>该街区房屋使用年代的中位数^2</td>\n",
       "      <td>0.000211725</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>该街区房屋使用年代的中位数 该街区平均的房间数目</td>\n",
       "      <td>-0.00126219</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>该街区房屋使用年代的中位数 该街区平均的卧室数目</td>\n",
       "      <td>0.0106115</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>该街区房屋使用年代的中位数 街区人口</td>\n",
       "      <td>2.81885e-06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>该街区房屋使用年代的中位数 平均入住率</td>\n",
       "      <td>-0.00181717</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>该街区房屋使用年代的中位数 街区的纬度</td>\n",
       "      <td>-0.010069</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>该街区房屋使用年代的中位数 街区的经度</td>\n",
       "      <td>-0.0099995</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>该街区平均的房间数目^2</td>\n",
       "      <td>0.00726948</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>该街区平均的房间数目 该街区平均的卧室数目</td>\n",
       "      <td>-0.0689064</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>该街区平均的房间数目 街区人口</td>\n",
       "      <td>-6.82366e-05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>该街区平均的房间数目 平均入住率</td>\n",
       "      <td>0.0268879</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>该街区平均的房间数目 街区的纬度</td>\n",
       "      <td>0.087509</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>该街区平均的房间数目 街区的经度</td>\n",
       "      <td>0.082289</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>该街区平均的卧室数目^2</td>\n",
       "      <td>0.160181</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>该街区平均的卧室数目 街区人口</td>\n",
       "      <td>0.000514264</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>该街区平均的卧室数目 平均入住率</td>\n",
       "      <td>-0.0871911</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>该街区平均的卧室数目 街区的纬度</td>\n",
       "      <td>-0.437043</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>该街区平均的卧室数目 街区的经度</td>\n",
       "      <td>-0.404151</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>街区人口^2</td>\n",
       "      <td>2.7378e-09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>街区人口 平均入住率</td>\n",
       "      <td>1.91427e-05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>街区人口 街区的纬度</td>\n",
       "      <td>2.2953e-05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>街区人口 街区的经度</td>\n",
       "      <td>1.46568e-05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>平均入住率^2</td>\n",
       "      <td>8.71561e-05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>平均入住率 街区的纬度</td>\n",
       "      <td>0.0213345</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>平均入住率 街区的经度</td>\n",
       "      <td>0.0162413</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>街区的纬度^2</td>\n",
       "      <td>0.0618867</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>街区的纬度 街区的经度</td>\n",
       "      <td>0.108107</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44</th>\n",
       "      <td>街区的经度^2</td>\n",
       "      <td>0.0399077</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                            0            1\n",
       "0                           1  5.91954e-08\n",
       "1                 该街区住户的收入中位数      -11.243\n",
       "2               该街区房屋使用年代的中位数    -0.848899\n",
       "3                  该街区平均的房间数目      6.44106\n",
       "4                  该街区平均的卧室数目     -31.5913\n",
       "5                        街区人口   0.00040609\n",
       "6                       平均入住率      1.00386\n",
       "7                       街区的纬度      8.70568\n",
       "8                       街区的经度      5.88063\n",
       "9               该街区住户的收入中位数^2   -0.0313081\n",
       "10  该街区住户的收入中位数 该街区房屋使用年代的中位数   0.00185995\n",
       "11     该街区住户的收入中位数 该街区平均的房间数目     0.043302\n",
       "12     该街区住户的收入中位数 该街区平均的卧室数目    -0.186142\n",
       "13           该街区住户的收入中位数 街区人口  5.72832e-05\n",
       "14          该街区住户的收入中位数 平均入住率   -0.0025902\n",
       "15          该街区住户的收入中位数 街区的纬度    -0.152506\n",
       "16          该街区住户的收入中位数 街区的经度    -0.144243\n",
       "17            该街区房屋使用年代的中位数^2  0.000211725\n",
       "18   该街区房屋使用年代的中位数 该街区平均的房间数目  -0.00126219\n",
       "19   该街区房屋使用年代的中位数 该街区平均的卧室数目    0.0106115\n",
       "20         该街区房屋使用年代的中位数 街区人口  2.81885e-06\n",
       "21        该街区房屋使用年代的中位数 平均入住率  -0.00181717\n",
       "22        该街区房屋使用年代的中位数 街区的纬度    -0.010069\n",
       "23        该街区房屋使用年代的中位数 街区的经度   -0.0099995\n",
       "24               该街区平均的房间数目^2   0.00726948\n",
       "25      该街区平均的房间数目 该街区平均的卧室数目   -0.0689064\n",
       "26            该街区平均的房间数目 街区人口 -6.82366e-05\n",
       "27           该街区平均的房间数目 平均入住率    0.0268879\n",
       "28           该街区平均的房间数目 街区的纬度     0.087509\n",
       "29           该街区平均的房间数目 街区的经度     0.082289\n",
       "30               该街区平均的卧室数目^2     0.160181\n",
       "31            该街区平均的卧室数目 街区人口  0.000514264\n",
       "32           该街区平均的卧室数目 平均入住率   -0.0871911\n",
       "33           该街区平均的卧室数目 街区的纬度    -0.437043\n",
       "34           该街区平均的卧室数目 街区的经度    -0.404151\n",
       "35                     街区人口^2   2.7378e-09\n",
       "36                 街区人口 平均入住率  1.91427e-05\n",
       "37                 街区人口 街区的纬度   2.2953e-05\n",
       "38                 街区人口 街区的经度  1.46568e-05\n",
       "39                    平均入住率^2  8.71561e-05\n",
       "40                平均入住率 街区的纬度    0.0213345\n",
       "41                平均入住率 街区的经度    0.0162413\n",
       "42                    街区的纬度^2    0.0618867\n",
       "43                街区的纬度 街区的经度     0.108107\n",
       "44                    街区的经度^2    0.0399077"
      ]
     },
     "execution_count": 112,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "coeff = pd.DataFrame([poly.get_feature_names(X.columns),reg.coef_.tolist()]).T\n",
    "coeff"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "favorite-plumbing",
   "metadata": {},
   "source": [
    "### 与变换前的模型拟合效果进行比对"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "id": "mighty-alignment",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 1.00000000e+00,  8.32520000e+00,  4.10000000e+01, ...,\n",
       "         1.43489440e+03, -4.63007240e+03,  1.49401729e+04],\n",
       "       [ 1.00000000e+00,  8.30140000e+00,  2.10000000e+01, ...,\n",
       "         1.43337960e+03, -4.62724920e+03,  1.49377284e+04],\n",
       "       [ 1.00000000e+00,  7.25740000e+00,  5.20000000e+01, ...,\n",
       "         1.43262250e+03, -4.62678400e+03,  1.49426176e+04],\n",
       "       ...,\n",
       "       [ 1.00000000e+00,  1.70000000e+00,  1.70000000e+01, ...,\n",
       "         1.55472490e+03, -4.77970460e+03,  1.46942884e+04],\n",
       "       [ 1.00000000e+00,  1.86720000e+00,  1.80000000e+01, ...,\n",
       "         1.55472490e+03, -4.78364760e+03,  1.47185424e+04],\n",
       "       [ 1.00000000e+00,  2.38860000e+00,  1.60000000e+01, ...,\n",
       "         1.54999690e+03, -4.77321880e+03,  1.46991376e+04]])"
      ]
     },
     "execution_count": 114,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#变换前的模型拟合效果\n",
    "poly = PolynomialFeatures(2).fit(X,y)\n",
    "X_ = poly.transform(X)\n",
    "X_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "id": "local-dining",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.6062326851998049"
      ]
     },
     "execution_count": 117,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lr = LinearRegression().fit(X,y)\n",
    "lr.score(X,y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "id": "contained-spouse",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.6832976293317485"
      ]
     },
     "execution_count": 119,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lr1 = LinearRegression().fit(X_,y)\n",
    "lr1.score(X_,y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "sacred-schedule",
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